packages_to_check <- c("stars", "httr", "jsonlite", "tmap", "basemaps")
# Check and install packages
for (package_name in packages_to_check) {
if (!package_name %in% rownames(installed.packages())) {
install.packages(package_name)
cat(paste("Package", package_name, "installed.\n"))
} else {
cat(paste("Package", package_name, "is already installed.\n"))
}
library(package_name, character.only = TRUE)
}
#in case tmap does not install
#remotes::install_github('r-tmap/tmap')MODIS NRT Global Flood Product
LANCE MODIS Near Real Time (NRT) Global Flood Product
Overview
In this lesson, you will use R to take a closer look at the data from the LANCE MODIS Near Real Time (NRT) Global Flood Product, including learning about what are LANCE and MODIS, and the NRT Flood products available. You will then learn to select, download, and visualize one of the NRT Flood layers…
Coding Review
This lesson uses the R language and environment. R is a popular language used for statistical computing and graphics.
Learning Objectives
After completing this lesson, you should be able to:
Determine what NRT raster data is available by navigating the LANCE website.
Read a tile map and select a raster tile to download based on a point of interest.
Download near-real-time raster data using the application programming interfaces (APIs) with the GET HTTP request method (Wilson 2024).
View the downloaded raster data to quickly preview.
Classify and place on a map the NRT flood raster data to determine areas with unusual flooding.
Introduction
Atmospheric circulation, water evaporation, and their interactions with land surfaces can impact a region’s rainfall variability. For example, California’s winter is correlated to ocean evaporation near the west coast and eastern North Pacific, and ocean evaporation is a strong factor in increased flooding in the region (Wei et al. 2016). Additionally, drought in the region is associated to high pressure systems off the U.S. west coast, with studies finding that the high pressure system is linked to the Pacific sea surface temperature anomalies, and exacerbated by high evaporation over land due to high temperatures.
It is critical to understand the water cycle and how flooding events develop, particularly as climate change intensifies extreme weather events, because the impacts of flooding can be a risk to human life, and can disrupt infrastructure, agriculture, and natural habitats.
The MODIS/Aqua+Terra Global Flood Product L3 Near Real Time (NRT) 250m Global Flood Product (MCDWD_L3_NRT) (beta) provides daily maps of flooding globally. The product is provided over 3 compositing periods (1-day, 2-day, and 3-day) to minimize the impact of clouds and more rigorously identify flood water (the best composite will depend on the cloudiness for a particular event) (Lin et al. 2019)
What are MODIS and LANCE?
MODIS
The Moderate Resolution Imaging Spectroradiometer (MODIS) is a NASA Earth Observing System (EOS) satellite-based sensor system that creates data products including land surface temperatures, land surface reflectance, radiances, clouds, aerosols, water vapor, active fire, snow cover, sea ice measurements, and other factor information. The MODIS Near Real-Time (NRT) data includes the Flood product which is a daily ~250 m resolution product showing flood and surface water detected from the twice-daily overpass of the MODIS optical sensors.
The satellite data is readily available shortly after it is acquired by the MODIS instrument on board the Terra and Aqua satellites. This space-based instrument distinguishes 36 spectral bands and groups of wavelengths which helps map the extent of snow and ice caused by winter storms and frigid temperatures. Initially, the water-detecting algorithm is applied to both MODIS observations (Terra and Aqua). Due to cloud and terrain shadows create false positives.
To minimize errors, the product is generated with three different compositing periods (1-day, 2-day, and 3-day) to compare results and decide which product has better coverage for the event. Further, they have to differentiate floods from expected surface water through the use of MODIS Land Water Mask (MOD44W), which uses a decision tree classifier trained with MODIS data to produce a global water mask (Carroll et al. 2016).
MODIS adoption aimed to surpass barriers related to satellite data, including cost, delivery timelines, limited formats, and the need for technical expertise. The transition to GFIMS establishes an operational system at FAO, ensuring continuity in meeting NASA data-user needs (Lin et al. 2019).
LANCE
The Land, Atmosphere Near Real-time Capability for EOS (LANCE) is a NASA initiative that provides near real-time access to satellite data, including MODIS. It allows users to access the latest data within a few hours of satellite overpass, enabling rapid responses to environmental events such as floods. LANCE is particularly valuable for emergency response teams and researchers who require up-to-date information for monitoring and assessing natural disasters (Land 2024).
LANCE reduces processing time, allowing for timely computation. Users access the data through platforms like Web Map Service (WMS) and Web Coverage Service (WCS), enabling visualization and analysis for informed decision-making. This NRT approach enhances the speed and accessibility of critical information on vegetation conditions (Zhang et al. 2022).
MODIS NRT Flood MCDWD Data Products
Data Information
The main landing pages for the MODIS NRT Global Flood Product:
The MODIS/Terra+Aqua Combined MODIS Water Detection (MCDWD) algorithm is tailor-made for detecting water bodies using MODIS data obtained from both the Terra and Aqua satellites. This algorithm employs various bands and spectral information to effectively identify and categorize water bodies. This enhances the accuracy and reliability of the flood product generated (Slayback 2023).
The MODIS Near Real-Time (NRT) Flood dataset offers multiple products, each accompanied by corresponding layers. The specific layers depend on the temporal aggregation:
MCDWD_F1_L3_NRT (1-Day product) This product type is the most basic level and provides binary information about water occurrence. Pixels are classified as either containing water or not, offering a simple way to identify flooded areas.
MCDWD_F1CS_L3_NRT (1-Day CS): F1CS has a cloud shadow mask applied on the version of the MCDWD_F1_L3_NRT product
MCDWD_F2_L3_NRT (2-Day): F2 provides additional information by categorizing water occurrences into three categories: no water, low-confidence water, and high-confidence water. This classification allows for a more nuanced understanding of the extent of the flood and its associated confidence levels.
MCDWD_F3_L3_NRT (3-Day): The F3 product, based on the MCDWD (MODIS/Terra+Aqua Combined MODIS Water Detection) is an algorithm that further refines flood mapping by adding additional spectral information. These results create a more accurate representation of water bodies and flooded areas (Slayback 2023).
Exploring the Data
For this exercise, we will be using the MCDWD L3 F3 product: LANCE NRT Flood
First, install and load the R packages required for this exercise:
Check what days are available for the MCDWD L3 F3 product by going to this link: LANCE NRT Primary Server or Secondary.
Based on availability, edit the year_day variable YYYY-DD. Example: ‘2022-01’
#add the year and date you want to search for (YYYY-DD, 2022-01)
year_day <- '2024-067'Determine tiles of interest:
Based on availability, edit the tile_code variable:
#add tile code from the map above (written as h00v00)
tile_code <- 'h05v05'This is the NRT Flood F3 (MCDWD_L3_F3) API URL:
# Primary Server
API_URL <- paste0('https://nrt3.modaps.eosdis.nasa.gov/api/v2/content/details?products=MCDWD_L3_F3_NRT&archiveSets=61&temporalRanges=')
# if the primary server is down, the secondary server may be available:
#API_URL <- paste0('https://nrt4.modaps.eosdis.nasa.gov/api/v2/content/details?products=MCDWD_L3_F3_NRT&archiveSets=61&temporalRanges=')We can combine the API URL above with the year_day provided and print the available datasets:
#pasting together URL and year_day
url <- paste0(API_URL, year_day)
print(url)[1] "https://nrt3.modaps.eosdis.nasa.gov/api/v2/content/details?products=MCDWD_L3_F3_NRT&archiveSets=61&temporalRanges=2024-067"Loading the Data
NASA earthdata access requires a nasa username and token. How to generate User Token.
Access the NASA Earthdata with the GET function. Replace USER_TOKEN with the token you generated:
# Define headers (include your token)
headers <- c(
Authorization = "Bearer USER_TOKEN"
)
# Make GET request
response <- httr::GET(url, httr::add_headers(.headers=headers))Check the response status from the GET function:
responseResponse [https://nrt3.modaps.eosdis.nasa.gov/api/v2/content/details?products=MCDWD_L3_F3_NRT&archiveSets=61&temporalRanges=2024-067]
Date: 2024-03-08 03:15
Status: 200
Content-Type: application/json;charset=UTF-8
Size: 113 kBOut of the response from the server, we’ll check if the response was a success with if (http_status(response)$category == "Success"). If this statement is true, then the content will be assigned to the variable data in JSON format, which is then parsed to a data frame using data_parsed <- jsonlite::fromJSON(data). The data frame contains data_parsed$content, a column with content. We filter the content by tile code using the command content_items <- data_parsed$content[grepl(tile_code, data_parsed$content$name, ignore.case = TRUE), ] and add the results to a data frame.
if (httr::http_status(response)$category == "Success") {
# Read the JSON content into a data frame
df <- httr::content(response, as = "parsed", simplifyVector = TRUE)
df <- df$content
} else {
print("Request failed with status code", httr::http_status(response)$status_code)
}
df archiveSets cksum collections dataDay
1 61 911102925 modis-nrt-c6.1 2024-067 = 2024-03-07
2 61 4183830999 modis-nrt-c6.1 2024-067 = 2024-03-07
3 61 4176744384 modis-nrt-c6.1 2024-067 = 2024-03-07
4 61 1297672265 modis-nrt-c6.1 2024-067 = 2024-03-07
5 61 1753908263 modis-nrt-c6.1 2024-067 = 2024-03-07
6 61 3932759360 modis-nrt-c6.1 2024-067 = 2024-03-07
7 61 819401368 modis-nrt-c6.1 2024-067 = 2024-03-07
8 61 3625785449 modis-nrt-c6.1 2024-067 = 2024-03-07
9 61 407671058 modis-nrt-c6.1 2024-067 = 2024-03-07
10 61 20861896 modis-nrt-c6.1 2024-067 = 2024-03-07
11 61 601490806 modis-nrt-c6.1 2024-067 = 2024-03-07
12 61 288859234 modis-nrt-c6.1 2024-067 = 2024-03-07
13 61 2282666467 modis-nrt-c6.1 2024-067 = 2024-03-07
14 61 3376155605 modis-nrt-c6.1 2024-067 = 2024-03-07
15 61 3230536203 modis-nrt-c6.1 2024-067 = 2024-03-07
16 61 2672028257 modis-nrt-c6.1 2024-067 = 2024-03-07
17 61 805411042 modis-nrt-c6.1 2024-067 = 2024-03-07
18 61 1787060908 modis-nrt-c6.1 2024-067 = 2024-03-07
19 61 4006115335 modis-nrt-c6.1 2024-067 = 2024-03-07
20 61 2384781496 modis-nrt-c6.1 2024-067 = 2024-03-07
21 61 4163055108 modis-nrt-c6.1 2024-067 = 2024-03-07
22 61 63322422 modis-nrt-c6.1 2024-067 = 2024-03-07
23 61 1442246687 modis-nrt-c6.1 2024-067 = 2024-03-07
24 61 3946305025 modis-nrt-c6.1 2024-067 = 2024-03-07
25 61 1457590316 modis-nrt-c6.1 2024-067 = 2024-03-07
26 61 25464367 modis-nrt-c6.1 2024-067 = 2024-03-07
27 61 2932433143 modis-nrt-c6.1 2024-067 = 2024-03-07
28 61 1814825663 modis-nrt-c6.1 2024-067 = 2024-03-07
29 61 1032529483 modis-nrt-c6.1 2024-067 = 2024-03-07
30 61 2258505952 modis-nrt-c6.1 2024-067 = 2024-03-07
31 61 897780179 modis-nrt-c6.1 2024-067 = 2024-03-07
32 61 2215779916 modis-nrt-c6.1 2024-067 = 2024-03-07
33 61 915609056 modis-nrt-c6.1 2024-067 = 2024-03-07
34 61 1321866270 modis-nrt-c6.1 2024-067 = 2024-03-07
35 61 2423438125 modis-nrt-c6.1 2024-067 = 2024-03-07
36 61 1272319923 modis-nrt-c6.1 2024-067 = 2024-03-07
37 61 1692701502 modis-nrt-c6.1 2024-067 = 2024-03-07
38 61 3628974917 modis-nrt-c6.1 2024-067 = 2024-03-07
39 61 3313650152 modis-nrt-c6.1 2024-067 = 2024-03-07
40 61 609640785 modis-nrt-c6.1 2024-067 = 2024-03-07
41 61 3216653147 modis-nrt-c6.1 2024-067 = 2024-03-07
42 61 161871670 modis-nrt-c6.1 2024-067 = 2024-03-07
43 61 3150588785 modis-nrt-c6.1 2024-067 = 2024-03-07
44 61 1379241840 modis-nrt-c6.1 2024-067 = 2024-03-07
45 61 769194949 modis-nrt-c6.1 2024-067 = 2024-03-07
46 61 4056169898 modis-nrt-c6.1 2024-067 = 2024-03-07
47 61 99622509 modis-nrt-c6.1 2024-067 = 2024-03-07
48 61 12187227 modis-nrt-c6.1 2024-067 = 2024-03-07
49 61 3239305804 modis-nrt-c6.1 2024-067 = 2024-03-07
50 61 1374206525 modis-nrt-c6.1 2024-067 = 2024-03-07
51 61 1226338403 modis-nrt-c6.1 2024-067 = 2024-03-07
52 61 3633766312 modis-nrt-c6.1 2024-067 = 2024-03-07
53 61 171012989 modis-nrt-c6.1 2024-067 = 2024-03-07
54 61 4144196374 modis-nrt-c6.1 2024-067 = 2024-03-07
55 61 527623442 modis-nrt-c6.1 2024-067 = 2024-03-07
56 61 3711230324 modis-nrt-c6.1 2024-067 = 2024-03-07
57 61 3707135396 modis-nrt-c6.1 2024-067 = 2024-03-07
58 61 2453642012 modis-nrt-c6.1 2024-067 = 2024-03-07
59 61 2291140968 modis-nrt-c6.1 2024-067 = 2024-03-07
60 61 1829651988 modis-nrt-c6.1 2024-067 = 2024-03-07
61 61 509955188 modis-nrt-c6.1 2024-067 = 2024-03-07
62 61 50465168 modis-nrt-c6.1 2024-067 = 2024-03-07
63 61 3349779350 modis-nrt-c6.1 2024-067 = 2024-03-07
64 61 4174245642 modis-nrt-c6.1 2024-067 = 2024-03-07
65 61 2795107408 modis-nrt-c6.1 2024-067 = 2024-03-07
66 61 3768436886 modis-nrt-c6.1 2024-067 = 2024-03-07
67 61 2129400770 modis-nrt-c6.1 2024-067 = 2024-03-07
68 61 3247442754 modis-nrt-c6.1 2024-067 = 2024-03-07
69 61 1166502646 modis-nrt-c6.1 2024-067 = 2024-03-07
70 61 3414529246 modis-nrt-c6.1 2024-067 = 2024-03-07
71 61 3499252916 modis-nrt-c6.1 2024-067 = 2024-03-07
72 61 83758025 modis-nrt-c6.1 2024-067 = 2024-03-07
73 61 3281324912 modis-nrt-c6.1 2024-067 = 2024-03-07
74 61 1431429996 modis-nrt-c6.1 2024-067 = 2024-03-07
75 61 3883573592 modis-nrt-c6.1 2024-067 = 2024-03-07
76 61 2444037848 modis-nrt-c6.1 2024-067 = 2024-03-07
77 61 531602058 modis-nrt-c6.1 2024-067 = 2024-03-07
78 61 4018160863 modis-nrt-c6.1 2024-067 = 2024-03-07
79 61 1133407369 modis-nrt-c6.1 2024-067 = 2024-03-07
80 61 869224745 modis-nrt-c6.1 2024-067 = 2024-03-07
81 61 666463446 modis-nrt-c6.1 2024-067 = 2024-03-07
82 61 555799993 modis-nrt-c6.1 2024-067 = 2024-03-07
83 61 1543489410 modis-nrt-c6.1 2024-067 = 2024-03-07
84 61 3952265861 modis-nrt-c6.1 2024-067 = 2024-03-07
85 61 1590627147 modis-nrt-c6.1 2024-067 = 2024-03-07
86 61 111322505 modis-nrt-c6.1 2024-067 = 2024-03-07
87 61 736697218 modis-nrt-c6.1 2024-067 = 2024-03-07
88 61 256068857 modis-nrt-c6.1 2024-067 = 2024-03-07
89 61 2216571856 modis-nrt-c6.1 2024-067 = 2024-03-07
90 61 3346704328 modis-nrt-c6.1 2024-067 = 2024-03-07
91 61 3635598827 modis-nrt-c6.1 2024-067 = 2024-03-07
92 61 2674047790 modis-nrt-c6.1 2024-067 = 2024-03-07
93 61 592987022 modis-nrt-c6.1 2024-067 = 2024-03-07
94 61 748618151 modis-nrt-c6.1 2024-067 = 2024-03-07
95 61 4121787492 modis-nrt-c6.1 2024-067 = 2024-03-07
96 61 3920697180 modis-nrt-c6.1 2024-067 = 2024-03-07
97 61 1944412415 modis-nrt-c6.1 2024-067 = 2024-03-07
98 61 1433114778 modis-nrt-c6.1 2024-067 = 2024-03-07
99 61 2786510581 modis-nrt-c6.1 2024-067 = 2024-03-07
100 61 271643490 modis-nrt-c6.1 2024-067 = 2024-03-07
101 61 325202520 modis-nrt-c6.1 2024-067 = 2024-03-07
102 61 2509589463 modis-nrt-c6.1 2024-067 = 2024-03-07
103 61 4179331921 modis-nrt-c6.1 2024-067 = 2024-03-07
104 61 1809936426 modis-nrt-c6.1 2024-067 = 2024-03-07
105 61 1046461766 modis-nrt-c6.1 2024-067 = 2024-03-07
106 61 1255517816 modis-nrt-c6.1 2024-067 = 2024-03-07
107 61 2272246792 modis-nrt-c6.1 2024-067 = 2024-03-07
108 61 2918704715 modis-nrt-c6.1 2024-067 = 2024-03-07
109 61 2169543186 modis-nrt-c6.1 2024-067 = 2024-03-07
110 61 1412857437 modis-nrt-c6.1 2024-067 = 2024-03-07
111 61 3266652666 modis-nrt-c6.1 2024-067 = 2024-03-07
112 61 608393956 modis-nrt-c6.1 2024-067 = 2024-03-07
113 61 2634255848 modis-nrt-c6.1 2024-067 = 2024-03-07
114 61 4065957404 modis-nrt-c6.1 2024-067 = 2024-03-07
115 61 4185182534 modis-nrt-c6.1 2024-067 = 2024-03-07
116 61 624941647 modis-nrt-c6.1 2024-067 = 2024-03-07
117 61 2128860858 modis-nrt-c6.1 2024-067 = 2024-03-07
118 61 3533902485 modis-nrt-c6.1 2024-067 = 2024-03-07
119 61 4291995361 modis-nrt-c6.1 2024-067 = 2024-03-07
120 61 3011591925 modis-nrt-c6.1 2024-067 = 2024-03-07
121 61 828362697 modis-nrt-c6.1 2024-067 = 2024-03-07
122 61 973658628 modis-nrt-c6.1 2024-067 = 2024-03-07
123 61 516848519 modis-nrt-c6.1 2024-067 = 2024-03-07
124 61 3149068785 modis-nrt-c6.1 2024-067 = 2024-03-07
125 61 1624577251 modis-nrt-c6.1 2024-067 = 2024-03-07
126 61 2543984269 modis-nrt-c6.1 2024-067 = 2024-03-07
127 61 3404562070 modis-nrt-c6.1 2024-067 = 2024-03-07
128 61 2878419211 modis-nrt-c6.1 2024-067 = 2024-03-07
129 61 1698287818 modis-nrt-c6.1 2024-067 = 2024-03-07
130 61 990712289 modis-nrt-c6.1 2024-067 = 2024-03-07
131 61 2743381887 modis-nrt-c6.1 2024-067 = 2024-03-07
132 61 3206824771 modis-nrt-c6.1 2024-067 = 2024-03-07
133 61 3818010868 modis-nrt-c6.1 2024-067 = 2024-03-07
134 61 3869180759 modis-nrt-c6.1 2024-067 = 2024-03-07
135 61 4145500826 modis-nrt-c6.1 2024-067 = 2024-03-07
136 61 1708619081 modis-nrt-c6.1 2024-067 = 2024-03-07
137 61 1122746475 modis-nrt-c6.1 2024-067 = 2024-03-07
138 61 2707547723 modis-nrt-c6.1 2024-067 = 2024-03-07
139 61 3657202178 modis-nrt-c6.1 2024-067 = 2024-03-07
140 61 3742100399 modis-nrt-c6.1 2024-067 = 2024-03-07
141 61 558774015 modis-nrt-c6.1 2024-067 = 2024-03-07
142 61 721276956 modis-nrt-c6.1 2024-067 = 2024-03-07
143 61 782666371 modis-nrt-c6.1 2024-067 = 2024-03-07
144 61 448644003 modis-nrt-c6.1 2024-067 = 2024-03-07
145 61 3125805774 modis-nrt-c6.1 2024-067 = 2024-03-07
146 61 505116820 modis-nrt-c6.1 2024-067 = 2024-03-07
147 61 304958144 modis-nrt-c6.1 2024-067 = 2024-03-07
148 61 575857815 modis-nrt-c6.1 2024-067 = 2024-03-07
149 61 1009254639 modis-nrt-c6.1 2024-067 = 2024-03-07
150 61 3486815419 modis-nrt-c6.1 2024-067 = 2024-03-07
151 61 3458666581 modis-nrt-c6.1 2024-067 = 2024-03-07
152 61 1974285922 modis-nrt-c6.1 2024-067 = 2024-03-07
153 61 3472960468 modis-nrt-c6.1 2024-067 = 2024-03-07
154 61 2377221185 modis-nrt-c6.1 2024-067 = 2024-03-07
155 61 1499876575 modis-nrt-c6.1 2024-067 = 2024-03-07
156 61 2547388920 modis-nrt-c6.1 2024-067 = 2024-03-07
157 61 886130574 modis-nrt-c6.1 2024-067 = 2024-03-07
158 61 4169562372 modis-nrt-c6.1 2024-067 = 2024-03-07
159 61 614201336 modis-nrt-c6.1 2024-067 = 2024-03-07
160 61 709063548 modis-nrt-c6.1 2024-067 = 2024-03-07
161 61 2327714217 modis-nrt-c6.1 2024-067 = 2024-03-07
162 61 663255262 modis-nrt-c6.1 2024-067 = 2024-03-07
163 61 262309265 modis-nrt-c6.1 2024-067 = 2024-03-07
164 61 458879417 modis-nrt-c6.1 2024-067 = 2024-03-07
165 61 751424680 modis-nrt-c6.1 2024-067 = 2024-03-07
166 61 2594457266 modis-nrt-c6.1 2024-067 = 2024-03-07
167 61 2447605694 modis-nrt-c6.1 2024-067 = 2024-03-07
168 61 2945462833 modis-nrt-c6.1 2024-067 = 2024-03-07
169 61 111311965 modis-nrt-c6.1 2024-067 = 2024-03-07
170 61 3478141266 modis-nrt-c6.1 2024-067 = 2024-03-07
171 61 115616276 modis-nrt-c6.1 2024-067 = 2024-03-07
172 61 2953545090 modis-nrt-c6.1 2024-067 = 2024-03-07
173 61 361942030 modis-nrt-c6.1 2024-067 = 2024-03-07
174 61 1047916622 modis-nrt-c6.1 2024-067 = 2024-03-07
175 61 2962341121 modis-nrt-c6.1 2024-067 = 2024-03-07
176 61 1142093054 modis-nrt-c6.1 2024-067 = 2024-03-07
177 61 754409895 modis-nrt-c6.1 2024-067 = 2024-03-07
178 61 2991083947 modis-nrt-c6.1 2024-067 = 2024-03-07
179 61 1006110089 modis-nrt-c6.1 2024-067 = 2024-03-07
180 61 2765901630 modis-nrt-c6.1 2024-067 = 2024-03-07
181 61 3386184360 modis-nrt-c6.1 2024-067 = 2024-03-07
182 61 2823219449 modis-nrt-c6.1 2024-067 = 2024-03-07
183 61 3384192099 modis-nrt-c6.1 2024-067 = 2024-03-07
184 61 2353884981 modis-nrt-c6.1 2024-067 = 2024-03-07
185 61 4240689386 modis-nrt-c6.1 2024-067 = 2024-03-07
186 61 2949667013 modis-nrt-c6.1 2024-067 = 2024-03-07
187 61 1315391317 modis-nrt-c6.1 2024-067 = 2024-03-07
188 61 971045979 modis-nrt-c6.1 2024-067 = 2024-03-07
189 61 4178241495 modis-nrt-c6.1 2024-067 = 2024-03-07
190 61 501247299 modis-nrt-c6.1 2024-067 = 2024-03-07
191 61 2119232607 modis-nrt-c6.1 2024-067 = 2024-03-07
192 61 2547095279 modis-nrt-c6.1 2024-067 = 2024-03-07
193 61 607285370 modis-nrt-c6.1 2024-067 = 2024-03-07
194 61 3112875710 modis-nrt-c6.1 2024-067 = 2024-03-07
195 61 3334267769 modis-nrt-c6.1 2024-067 = 2024-03-07
196 61 3637912597 modis-nrt-c6.1 2024-067 = 2024-03-07
197 61 4148878259 modis-nrt-c6.1 2024-067 = 2024-03-07
198 61 2527156434 modis-nrt-c6.1 2024-067 = 2024-03-07
199 61 365761083 modis-nrt-c6.1 2024-067 = 2024-03-07
200 61 3131787596 modis-nrt-c6.1 2024-067 = 2024-03-07
201 61 3612317385 modis-nrt-c6.1 2024-067 = 2024-03-07
202 61 1478376525 modis-nrt-c6.1 2024-067 = 2024-03-07
203 61 478944668 modis-nrt-c6.1 2024-067 = 2024-03-07
204 61 1141757361 modis-nrt-c6.1 2024-067 = 2024-03-07
205 61 1344330535 modis-nrt-c6.1 2024-067 = 2024-03-07
206 61 392948103 modis-nrt-c6.1 2024-067 = 2024-03-07
207 61 1049738651 modis-nrt-c6.1 2024-067 = 2024-03-07
208 61 4200200191 modis-nrt-c6.1 2024-067 = 2024-03-07
209 61 1257213428 modis-nrt-c6.1 2024-067 = 2024-03-07
210 61 193473035 modis-nrt-c6.1 2024-067 = 2024-03-07
211 61 4285861721 modis-nrt-c6.1 2024-067 = 2024-03-07
212 61 3549622105 modis-nrt-c6.1 2024-067 = 2024-03-07
213 61 2971319130 modis-nrt-c6.1 2024-067 = 2024-03-07
214 61 2848495691 modis-nrt-c6.1 2024-067 = 2024-03-07
215 61 2898112731 modis-nrt-c6.1 2024-067 = 2024-03-07
216 61 138917032 modis-nrt-c6.1 2024-067 = 2024-03-07
217 61 3416969796 modis-nrt-c6.1 2024-067 = 2024-03-07
218 61 901979245 modis-nrt-c6.1 2024-067 = 2024-03-07
219 61 1783228255 modis-nrt-c6.1 2024-067 = 2024-03-07
220 61 1933657732 modis-nrt-c6.1 2024-067 = 2024-03-07
221 61 1029176164 modis-nrt-c6.1 2024-067 = 2024-03-07
222 61 4294145227 modis-nrt-c6.1 2024-067 = 2024-03-07
223 61 1322881760 modis-nrt-c6.1 2024-067 = 2024-03-07
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223 /api/v2/content/details/MCDWD_L3_F3_NRT.A2024067.h10v07.061.tif 794864Search the Data Frame and subset the rows that contain tile_code in the downloadsLink column.
# Subset rows where the downloadsLink column contains the tile code
subset_df <- df[grepl(tile_code, df$downloadsLink), ]
subset_df archiveSets cksum collections dataDay
150 61 3486815419 modis-nrt-c6.1 2024-067 = 2024-03-07
downloadsLink
150 https://nrt3.modaps.eosdis.nasa.gov/api/v2/content/archives/MCDWD_L3_F3_NRT.A2024067.h05v05.061.tif
fileId md5sum mtime
150 2162289926 701fabe1354a4dcaf99248a89b49c74f 1709862445
name products resourceType
150 MCDWD_L3_F3_NRT.A2024067.h05v05.061.tif MCDWD_L3_F3_NRT File
self size
150 /api/v2/content/details/MCDWD_L3_F3_NRT.A2024067.h05v05.061.tif 905578If there is only 1 row, select the string ‘downloadsLink’ column:
download_link <- subset_df$downloadsLink
print(download_link)[1] "https://nrt3.modaps.eosdis.nasa.gov/api/v2/content/archives/MCDWD_L3_F3_NRT.A2024067.h05v05.061.tif"Read the Data
Use the “read_stars()” function from the “stars” R Library to read the geoTiff raster.
The raster is assigned to the “raster_df” variable:
raster_df <- stars::read_stars(download_link)Set the Coordinate Reference System (CRS) to a new raster “my_raster” to plot it in a map. For example, Web Mercator EPSG:3857. Use the st_transform() function:
raster_df <- sf::st_transform(raster_df, 3857)Now the raster data should be stored as a variable “my_raster” with the CRS set to Web Mercator EPSG:3857
Visualizing NRT Flood Data
Plot the raster to quickly view it:
plot(raster_df, axes = TRUE)
Create NRT Flood Plot with Classification
Refer to the MODIS NRT Global Flood Product User Guide for more information.
NRT Flood data has 5 classifications:
| Code | Definition |
|---|---|
| 0 | No Water |
| 1 | Surface Water |
| 2 | Recurring flood4 |
| 3 | Flood (unusual) |
| 255 | Insufficient data |
To view the data in this classification, we’ll need to create a classified legend; however, the NRT Flood data is stored in decimal numbers (aka floating-point). Create class breaks dividing the data by the following breaks, and corresponding colors and labels:
class_breaks <- c(-Inf, 0.1, 1.1, 2.1, 3.1)
colors <- c( "gray", "blue", "yellow", "red")
labels = c("0: No Water", "1: Surface Water", "2: Recurring flood", "3: Flood (unusual)")Add a title for the plot that includes the year, day of year, and tile code:
title = paste("Near Real-Time Flood F3", year_day, tile_code)Generate a basemap from Esri Imagery
To generate a basemap that shows the location of our raster, we must first create a bounding box to match raster_df.
bbox <- sf::st_bbox(raster_df)The basemap_stars() function from the stars library allows us to access Esri imagery layers. We choose “world_imagery” as our background and assign it to the object bm_m.
bm_m <- basemaps::basemap_stars(bbox, map_service = "esri", map_type = "world_imagery")Loading basemap 'world_imagery' from map service 'esri'...The st_rgb function lets us turn the RGB stars item into a single image
bm_m <- stars::st_rgb(bm_m)Plot basemap and NRT Flood data
Generate a plot from the tmap library using the tm_shape() function. We will plot the basemap and the raster_df items.
## tmap mode set to "plot"
tmap::tmap_mode("plot")
## tmap mode can also be set to "view"
#tmap_mode("view")
#create an object the plots the basemap and the NRT flood raster
#with the tmap library, call the tm_shape() function for the basemap
tm_plot <- tmap::tm_shape(bm_m)+
tmap::tm_raster()+
#create a new tmap shape for the NRT flood raster with style as "cat," meaning categorical.
tmap::tm_shape(raster_df, style="cat")+
#add the classification styling to the raster
tmap::tm_raster( palette = c(colors),
title = title,
breaks = class_breaks,
labels = labels )+
#style the plot
tmap::tm_layout(legend.outside = TRUE) +
tmap::tm_graticules(lines=FALSE)View the plot:
tm_plot
To observe a location closer, we can use the geoboundaries of any country, in this Case the USA.
usa <- httr::GET("https://www.geoboundaries.org/api/current/gbOpen/USA/ADM1/")After the GET call, we have to translate the content.
usa <- httr::content(usa)
names(usa) [1] "boundaryID" "boundaryName"
[3] "boundaryISO" "boundaryYearRepresented"
[5] "boundaryType" "boundaryCanonical"
[7] "boundarySource" "boundaryLicense"
[9] "licenseDetail" "licenseSource"
[11] "boundarySourceURL" "sourceDataUpdateDate"
[13] "buildDate" "Continent"
[15] "UNSDG-region" "UNSDG-subregion"
[17] "worldBankIncomeGroup" "admUnitCount"
[19] "meanVertices" "minVertices"
[21] "maxVertices" "meanPerimeterLengthKM"
[23] "minPerimeterLengthKM" "maxPerimeterLengthKM"
[25] "meanAreaSqKM" "minAreaSqKM"
[27] "maxAreaSqKM" "staticDownloadLink"
[29] "gjDownloadURL" "tjDownloadURL"
[31] "imagePreview" "simplifiedGeometryGeoJSON"The parsed content object contains 32 components. Item 29 is a direct link to the geojson file (gjDownloadURL). Read that in and check the visuals.
usa <- sf::st_read(usa$gjDownloadURL)Reading layer `geoBoundaries-USA-ADM1' from data source
`https://github.com/wmgeolab/geoBoundaries/raw/9469f09/releaseData/gbOpen/USA/ADM1/geoBoundaries-USA-ADM1.geojson'
using driver `GeoJSON'
Simple feature collection with 56 features and 5 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: -179.1489 ymin: -14.54869 xmax: 179.7785 ymax: 71.36516
Geodetic CRS: WGS 84plot(sf::st_geometry(usa))
This looks good, but it includes all United States territories. For simplicity, we can get it down to only California.
drops<-
c("California")
usa<-usa[(usa$shapeName %in% drops),]
plot(sf::st_geometry(usa))
We need to provide the same Coordinate Reference System (CRS)
usa <- sf::st_transform(usa, 3857)We can subset our RGB image with the California vector:
raster_cali <- bm_m[usa]## tmap mode set to "plot"
tmap::tmap_mode("plot")
## tmap mode can also be set to "view"
#tmap_mode("view")
#create an object the plots the basemap and the NRT flood raster
#with the tmap library, call the tm_shape() function for the basemap
tm_plot <- tmap::tm_shape(raster_cali)+
tmap::tm_raster()+
#create a new tmap shape for the NRT flood raster with style as "cat," meaning categorical.
tmap::tm_shape(raster_df, style="cat")+
#add the classification styling to the raster
tmap::tm_raster( palette = c(colors),
title = title,
breaks = class_breaks,
labels = labels )+
#style the plot
tmap::tm_layout(legend.outside = TRUE) +
tmap::tm_graticules(lines=FALSE)View the updated plot:
tm_plot
In this Lesson, You Learned…
Congratulations! Now you should be able to:
Navigate the LANCE data website and determine what data is available.
Select a tile and date to download NRT data.
Create a GET HTTP request to download near-real-time data.
Plot on a map and classify raster data to determine areas with unusual flooding.
References
Carroll, M. L., C. M. DiMiceli, J. R. G. Townshend, R. A. Sohlberg, A. I. Elders, S. Devadiga, A. M. Sayer, and R. C. Levy. 2016. “Development of an Operational Land Water Mask for MODIS Collection 6, and Influence on Downstream Data Products.” International Journal of Digital Earth 10 (2): 207–18. https://doi.org/10.1080/17538947.2016.1232756.
Land, Atmosphere Near real-time Capability for EO (LANCE). 2024. “About LANCE.” January 11, 2024. https://www.earthdata.nasa.gov/learn/find-data/near-real-time/about-lance.
Lin, Li, Liping Di, Junmei Tang, Eugene Yu, Chen Zhang, Md. Rahman, Ranjay Shrestha, and Lingjun Kang. 2019. “Improvement and Validation of NASA/MODIS NRT Global Flood Mapping.” Remote Sensing 11 (2): 205. https://doi.org/10.3390/rs11020205.
Slayback, D. 2023. “Modis NRT Global Flood Product - Earthdata.” https://www.earthdata.nasa.gov/s3fs-public/2023-01/MCDWD_UserGuide_RevC.pdf.
Wei, Jiangfeng, Qinjian Jin, Zong-Liang Yang, and Paul A. Dirmeyer. 2016. “Role of Ocean Evaporation in California Droughts and Floods.” Geophysical Research Letters 43 (12): 6554–62. https://doi.org/10.1002/2016gl069386.
Wilson, James. 2024. “NASA API Overview.” November 30, 2024. https://github.com/wilsjame/how-to-nasa?tab=readme-ov-file.
Zhang, Chen, Zhengwei Yang, Liping Di, Eugene G. Yu, Bei Zhang, Weiguo Han, Li Lin, and Liying Guo. 2022. “Near-Real-Time MODIS-Derived Vegetation Index Data Products and Online Services for CONUS Based on NASA LANCE.” Scientific Data 9 (1). https://doi.org/10.1038/s41597-022-01565-2.